miCDER: A Context-Aware Transformer Model for Joint miRNA-Disease Entity and Multi-level Regulatory Relation Extraction

Background: Dysregulation of microRNAs (miRNAs) is closely linked to the progression of diverse human diseases. However, the lack of a standardized, fine-grained dataset of miRNA–disease regulatory interactions and limited ability of existing methods to capture multi-level regulatory relations hinder full understanding of these mechanisms. Results: We constructed a fine-grained, multi-level annotated biomedical dataset covering ten entity types and thirteen relation categories, tailored for modeling microRNA–disease interactions. We then propose miCDER, a transformer-based model that jointly extracts entities and relations through contextual encoding and inter-span transformer attention. miCDER achieves state-of-the-art performance in both named entity recognition (NER F1 = 87.34%) and relation extraction (RE F1 = 77.28%), outperforming the SpERT baseline by 8.84% and 12.35%, respectively. Applied to 27,051 curated miRNA-related text segments from PubMed, miCDER automatically extracted 93,221 high-confidence regulatory triplets to construct the miRNA–disease knowledge graph, MAAD-HCD-KG. Among these, 1,735 targeting relations not found in microRNA target database (miRTarBase) demonstrate miCDER's ability to capture literature-supported regulatory associations that are not yet included in manually curated databases. Conclusion: Our proposed miCDER model can serve as an effective tool for extracting fine-grained regulatory information from biomedical texts. The resultant knowledge graph constitutes a significant resource for the advancement of research on diseases related to microRNA.